We measured performance using the building IoU-\(F_1\)  score defined in Van Etten et al. [1]}. Briefly, building footprint polygons were extracted from segmentation masks (or taken directly from object detection bounding box outputs) and compared to ground truth polygons. Predictions were labeled True Positive if they had an IoU with a ground truth polygon above 0.5 and all other predictions were deemed False Positives. Using these statistics and the number of undetected ground truth polygons (False Negatives), we calculated the precision \(P\)  and recall \(R\)  of the model predictions in aggregate. We then report the \(F_1\)  score as
\(F_1 = \frac{2 \times P \times R}{P + R}\) 
